In Study of Women’s Health Across the Nation (SWAN), we plan to conduct analyses relating to the impact of menopause using marginal structure and mixed models, recursive partitioning (CART), and survival analyses. I have the expertise, leadership and motivation necessary to successfully carry out the proposed work. I have a broad background in statistics, with training and expertise in key research areas for this application. I carried out primary and secondary data analysis on liver transplantation research for both the Thomas E. Starzl Transplant Institute as well as for the Liver Transplantation Database funded by NIDDK I have experience as the former lead biostatistician for the University of Pittsburgh Diabetes Institute where I developed a coronary risk engine for patients with type 1 diabetes. I collaborated with a team of experts to establish a large diabetes warehouse using a large administrative database. The diabetes warehouse consists of over 119 million charges, 95 million lab values, 467,000 visits as well as information pertaining to basic demographics, diabetes education, comorbid conditions and medication use. Collaborating researchers were able to explore their own data using Online Analytical Processing (OLAP), a technology that allows researchers to “slice and dice” the data based on their domain expertise and the parameters of interest. I was the lead statistician on an insulin intensification study funded by Sanofi-Aventis. The goal of this project was identifying patients with type 1 and type 2 from a large administrative dataset and then examining the patterns of insulin intensification. Trajectory analyses, marginal structure models and CART are methods are being utilized with this project. I also provide statistical consulting services for an orthopedic surgery department evaluating current treatment for osteoarthritis of the knee. Currently I am one of 4 PhD-level statisticians on the NIA funded SWAN project at the University of Pittsburgh’s Epidemiology Data Center. My key areas of expertise include bone loss and strength and fracture risk in women traversing the menopause. I also have expertise in medication coding and pharmaco-epidemiology research. Recently I worked with a team to code over 80,000 medications and assigned Iowa Drug Information System (IDIS) codes to each active ingredient contained in a medication. These data will now be utilized to examine if different classes of medications are associated with higher rates of bone loss across the menopause transition. They will also be utilized to examine difference in medication use between the different ethnic groups of women in SWAN. As a co-Investigator on several university-funded projects, I collaborated with many clinicians, statisticians and epidemiologists, which has led to successfully administered projects (e.g. staffing, research protections, budget), and peer-reviewed publications from each project. As a result of these previous experiences, I understand the importance of frequent communication among project members and of constructing realistic research plans. The current application builds on my prior work. In summary, I have a demonstrated a record of successful and productive research projects.
1987 University of Pittsburgh; BSN
1994 Duquesne University, MSN-Education
2004 University of Pittsburgh, Dr.PH- Biostatistics
EPIDEM 2185- INTRO TO SAS